Advances in distributed machine learning have brought forth the rise of Federated Learning (FL) [1]}, [2]} and Decentralized Learning (DL) [3]}, [4]}. While FL aims to train a centralized model using decentralized data, DL attempts to train decentralized models using decentralized data. In particular, DL involves the participation of decentralized peer-to-peer (p2p) nodes that collaboratively train their local models in a manner where all local models ideally converge on the global data distribution [5]}. A major issue in FL/DL environments is the inherent presence of non-Independent and Identically Distributed (non-IID) [1]} global data, commonly referred to as data heterogeneity.
